English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a two-step framework for predicting the implied volatility surfaceover time without static arbitrage. In the first step, we select features torepresent the surface and predict them over time. In the second step, we usethe predicted features to construct the implied volatility surface using a deepneural network (DNN) model by incorporating constraints that prevent staticarbitrage. We consider three methods to extract features from the impliedvolatility data: principal component analysis, variational autoencoder andsampling the surface, and we predict these features using LSTM. Using a longtime series of implied volatility data for S\&P500 index options to train ourmodels, we find two feature construction methods, sampling the surface andvariational autoencoders combined with DNN for surface construction, are thebest performers in out-of-sample prediction. In particular, they outperform aclassical method substantially. Furthermore, the DNN model for surfaceconstruction not only removes static arbitrage, but also significantly reducesthe prediction error compared with a standard interpolation method. Ourframework can also be used to simulate the dynamics of the implied volatilitysurface without static arbitrage.

A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface